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pyqmdnet.yaml
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pysot-tookit/requirements.txt
PyTorch implementation of FedTrack, which runs with a single CPU core and a single GPU (GTX 3080 Ti).
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training datasets: Imagenet-VID and vot2018\19\21
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testing datasets: OTB100, LaSOT, GOT-10k
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python 3.9+
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opencv 4.6+
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PyTorch 2.0+ and its dependencies
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for GPU support: a GPU with ~4G memory
python pretrain/fedtrain.py
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Visualization of the trace
python tracking/run_tracker.py -s Basketball [-d (display fig)] [-f (save fig)]
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You can provide a sequence configuration in two ways (see tracking/gen_config.py):
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python tracking/run_tracker.py -s [seq name]
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python tracking/run_tracker.py -j [json path]
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Draw success and precision
python pysot-tookit/pysot/visualization/draw_success_precision.py
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Pretraining on ImageNet-VID
- Download ImageNet-VID dataset into "datasets/ILSVRC"
python pretrain/prepro_imagenet.py python pretrain/train_mdnet.py -d imagenet